Harmful algal blooms (HABs) are more than seasonal nuisances – they are escalating threats to ecosystems, economies, and public health. 

Traditional monitoring often lags behind the rapid spread of HABs, leaving responders one step behind.

Our Approach: Precision Monitoring & Forecasting
Our AI models integrate satellite remote sensing and in-situ environmental measurements into scalable, high-frequency tools for ecosystem monitoring.

• They map bloom activity across lakes, with a 12-month rolling forecast, producing insights into the movement, extent, and risk factors for cyanobacterial blooms, enabling early, informed action. 

• We have also developed an explainability layer that provides insight into bloom causation, supporting targeted prevention. 

• The models have been designed with scalability in mind. Once calibrated using local in-situ data, they can operate autonomously, making them ideal for deployment in remote or under-resourced regions.

Tried and Tested
Our models were rigorously tested on the HABs at Lough Neagh (Northern Ireland).